Ada-FFL:Adaptive computing fairness federated learning

As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

Two-Stage Optimal Dispatching of Wind Power-Photovoltaic-Solar Thermal Combined System Considering Economic Optimality and Fairness

Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.

Resource allocation based on fairness and QoS provisioning for OFDMA-WLAN system

To satisfy different service requirements of multiple users in the orthogo nal frequency division multiple access wireless local area network OFDMA-WLAN system downlink transmission a resource allocation algorithm based on fairness and quality of service QoS provisioning is proposed. Different QoS requirements are converted into different rate requirements to calculate the QoSs atisfaction level.The optimization object is revised as a fairness-driven resource optimization function to provide fairness. The complex resource allocation problem is divided into channel allocation and power assignment sub-problems. The sub-problems are solved by the bipartite graph matching and water-filling based method.Compared with other algorithms the proposed algorithm sacrifices less data rate for higher fairnes and QoS satisfaction.The sim ulation results show that the proposed algorithm is capableo fp rovi ding QoS and fairness and performs better in a tradeoff among QoS fairness and data rate.

Integration of α-fairness with DEA based resource allocation model

How to allocate a resource efficiently and fairly attracts the attention of both researchers and practitioners. Data envelopment analysis（DEA） has been brought to bear on its solution. The existing literature applies Gini coefficient to measure the fairness in the resource allocation process. However, the Gini coefficient is inapplicable in many applications. This paper proposes a novel centralized resource allocation model based on DEA that considers both the efficiency and the fairness. This paper adopts a notion of fairness, namely α-fairness that is well studied in welfare economics and is of practical significance. The new model integratesα-fairness with DEA to support resource allocation decisions. It aids decision makers in making a trade-off between the efficiency and the fairness. An illustrative application is used to validate the proposed approach.

Fairness-Oriented Hybrid Precoding for Massive MIMO Maritime Downlink Systems with Large-Scale CSIT

Different from conventional cellular networks, a maritime communication base station(BS) has to cover a much wider area due to the limitation of available BS sites. Accordingly the performance of users far away from the BS is poor in general. This renders the fairness among users a challenging issue for maritime communications. In this paper, we consider a practical massive MIMO maritime BS with hybrid digital and analog precoding. Only the large-scale channel state information at the transmitter(CSIT) is considered so as to reduce the implementation complexity and overhead of the system. On this basis, we address the problem of fairness-oriented precoding design. A max-min optimization problem is formulated and solved in an iterative way. Simulation results demonstrate that the proposed scheme performs much better than conventional hybrid precoding algorithms in terms of minimum achievable rate of all the users, for the typical three-ray maritime channel model.

Proportional Fairness-Based Energy-Efficient Power Allocation in Downlink MIMO-NOMA Systems with Statistical CSI

In this paper, proportional fairness(PF)-based energy-efficient power allocation is studied for multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) systems. In our schemes, statistical channel state information(CSI) is utilized for perfect CSI is impossible to achieve in practice. PF is used to balance the transmission efficiency and user fairness. Energy efficiency(EE) is formulated under basic data rate requirements and maximum transmitting power constraints. Due to the non-convex nature of EE, a two-step algorithm is proposed to obtain sub-optimal solution with a low complexity. Firstly, power allocation is determined by golden section search for fixed power. Secondly total transmitting power is determined by fractional programming method in the feasible regions. Compared to the performance of MIMO-NOMA without PF constraint, fairness is obtained at expense of decreasing of EE.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

A Universal Fairness Evaluation Framework for Resource Allocation in Cloud Computing

In cloud computing,fairness is one of the most significant indicators to evaluate resource allocation algorithms,which reveals whether each user is allocated as much as that of all other users having the same bottleneck.However,how fair an allocation algorithm is remains an urgent issue.In this paper,we propose Dynamic Evaluation Framework for Fairness(DEFF),a framework to evaluate the fairness of an resource allocation algorithm.In our framework,two sub-models,Dynamic Demand Model(DDM) and Dynamic Node Model(DNM),are proposed to describe the dynamic characteristics of resource demand and the computing node number under cloud computing environment.Combining Fairness on Dominant Shares and the two sub-models above,we finally obtain DEFF.In our experiment,we adopt several typical resource allocation algorithms to prove the effectiveness on fairness evaluation by using the DEFF framework.

Power Splitting Based SWIPT in Network-Coded Two-Way Networks with Data Rate Fairness:An Information-Theoretic Perspective

This paper investigates the simultaneous wireless information and powertransfer(SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting(EH) relay. We present a power splitting(PS)-based two-way relaying(PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signalto-noise ratio(SNR), the performance loss is relatively small. Another observation is that, in relatively high SNR regime, PS-TWR outperforms time switching-based two-way relaying(TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.

A Discrimination Index Based on Jain's Fairness Index to Differentiate Researchers with Identical H-index Values

Purpose:This paper proposes a discrimination index method based on the Jain’s fairness index to distinguish researchers with the same H-index.Design/methodology/approach:A validity test is used to measure the correlation of D-offset with the parameters,i.e.H-index,the number of cited papers,the total number of citations,the number of indexed papers,and the number of uncited papers.The correlation test is based on the Saphiro-Wilk method and Pearson’s product-moment correlation.Findings:The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset(D-offset),with a range of D-offset from 0.00 to 0.99.The result of the correlation value between the D-offset and the number of uncited papers is 0.35,D-offset with the number of indexed papers is 0.24,and the number of cited papers is 0.27.The test provides the result that it is very unlikely that there exists no relationship between the parameters.Practical implications:For this reason,D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index.The H-index for researchers can be written with the format of“H-index:D-offset”.Originality/value:D-offset is worthy to be considered as a complement value to add the H-index value.If the D-offset is added in the H-index value,the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.

A Method for Assessing the Fairness of Health Resource Allocation Based on Geographical Grid

The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development.The data used in most of the existing assessment methods comes from statistical yearbooks or field survey sampling.These statistics are generally based on administrative areas and are difficult to support a fine-grained evaluation model.In response to these problems,the evaluation method proposed in this paper is based on the query statistics of the geographic grid of the target area,which are more accurate and efficient.Based on the query statistics of hot words in the geographic grids,this paper adopts the maximum likelihood estimation method to estimate the population in the grid region.Then,according to the statistical yearbook data of Hunan province,the estimated number and actual number of hospitals in each grid are analyzed and compared to measure the fairness of health resource allocation in the target region.Experiments show that the geographical grid population assessment based on hot words is more accurate and close to the actual value.The estimated average error is only about 17.8 percent.This method can assess the fairness of health resource allocation in any scale,and is innovative in data acquisition and evaluation methods.

A New Multi-Resource Allocation Mechanism： A Tradeoff between Fairness and Efficiency in Cloud Computing

This paper addresses multi-resource fair allocation: a fundamental research topic in cloud computing. To improve resource utilization under well-studied fairness constraints, we propose a new allocation mechanism called Dominant Resource with Bottlenecked Fairness(DRBF), which generalizes Bottleneck-aware Allocation(BAA) to the settings of Dominant Resource Fairness(DRF). We classify users into different queues by their dominant resources. The goals are to ensure that users in the same queue receive allocations in proportion to their fair shares while users in different queues receive allocations that maximize resource utilization subject to well-studied fairness properties such as those in DRF. Under DRBF, no user 1) is worse off sharing resources than dividing resources equally among all users; 2) prefers the allocation of another user; 3) can improve their own allocation without reducing other users' allocations; and(4) can benefit by misreporting their resource demands. Experiments demonstrate that the proposed allocation policy performs better in terms of high resource utilization than does DRF.

Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner.NOMA allows multiple terminals to share the same resource unit at the same time.The receiver usually needs to configure successive interference cancellation(SIC).The receiver eliminates co-channel interference(CCI)between users and it can significantly improve the system throughput.In order to meet the demands of users and improve fairness among them,this paper proposes a new power allocation scheme.The objective is to maximize user fairness by deploying the least fairness in multiplexed users.However,the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker(KKT)constraints.Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.

Proportional Fairness Based Energy Efficient Routing in Wireless Sensor Network

Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions,device monitoring, and collection of information. Due to limited energy resourcesavailable at SN, the primary issue is to present an energy-efficient framework andconserve the energy while constructing a route path along with each sensor node.However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routingwith minimal energy consumption in WSN. This paper presents an energy-efficientrouting system called Energy-aware Proportional Fairness Multi-user Routing(EPFMR) framework in WSN. EPFMR is deployed in the WSN environment usingthe instance time. The request time sent for the route discovery is the foremost stepdesigned in the EPFMR framework to reduce the energy consumption rate. Theproportional fairness routing in WSN selects the best route path for the packet flowbased on the relationship between the periods of requests between different SN.Route path discovered for packet flow also measure energy on multi-user route pathusing the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR developsnode dependent energy-efficient localized route path, improving the throughput.The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing,Throughput, RST, node density and average energy per packet in WSN. The RouteSearching Time (RST) is reduced using the Boltzmann Distribution (BD), and as aresult, the energy is minimized on multi-user WSN. Finally, GIFM applies aninstance time difference-based route searching on WSN to attain an optimal energyminimization system. Experimental analysis shows that the EPFMR framework canreduce the RST by 23.47% and improve the throughput by 6.79% compared withthe state-of-the-art works.

A New Fairness-Oriented Packet Scheduling Scheme with Reduced Channel Feedback for OFDMA Packet Radio Systems

In this paper, we propose a flexible and fairness-oriented packet scheduling approach for 3GPP UTRAN long term evolution (LTE) type packet radio systems, building on the ordinary proportional fair (PF) scheduling principle and channel quality indicator (CQI) feedback. Special emphasis is also put on practical feedback reporting mechanisms, including the effects of mobile measurement and estimation errors, reporting delays, and CQI quantization and compression. The performance of the overall scheduling and feedback re-porting process is investigated in details, in terms of cell throughput, coverage and resource allocation fairness, by using extensive quasistatic cellular system simulations in practical OFDMA system environment with frequency reuse of 1. The performance simulations show that by using the proposed modified PF ap-proach, significant coverage improvements in the order of 50% can be obtained at the expense of only 10-15% throughput loss, for all reduced feedback reporting schemes. This reflects highly improved fairness in the radio resource management (RRM) compared to other existing schedulers, without essentially com-promising the cell capacity. Furthermore, we demonstrate the improved functionality increase in radio re-source management for UE’s utilizing multi-antenna diversity receivers.

Mobile-Service Based Max-Min Fairness Resource Scheduling for Heterogeneous Vehicular Networks

This paper investigates the resource scheduling for heterogeneous vehicular networks, where some moving vehicles are selected and scheduled as helping relays to assist information transmission between the roadside infrastructure and other moving vehicles. For such a system, we propose a mobile-service based max-min fairness resource scheduling scheme, where service amount which is more suitable for high mobility scenarios is adopted to characterize the information transmission capacity of the links and the max-min criteria is adopted to meet the fairness requirement of the moving vehicles. Simulation results demonstrate the effectiveness of our proposed scheme. It is shown that our proposed scheme archives higher throughput and better fairness compared with random scheduling scheme and non-relaying scheme.

Exploiting Spectral-Energy Efficiency Tradeoff with Fairness in Non-Orthogonal Multiple Access Systems

The spectral efficiency(SE)and energy efficiency(EE)tradeoff while ensuring rate fairness among users in non-orthogonal multiple access(NOMA)systems is investigated.In order to characterize the SE-EE tradeoff with rate fairness,a multi-objective optimization(MOO)problem is first formulated,where the rate fairness is represented with theα-fair utility function.Then,the MOO problem is converted into a single-objective optimization(SOO)problem by the weighted sum method.To solve the converted non-convex SOO problem,we apply sequential convex programming,which helps to propose a general power allocation algorithm to realize the SE-EE tradeoff with rate fairness.We prove the convergence of the proposed algorithm and the convergent solution satisfies the KKT conditions.Simulation results demonstrate the proposed power allocation algorithm can achieve various levels of rate fairness,and higher fairness results in degraded performance of SE-EE tradeoff.A pivotal conclusion is reached that NOMA systems significantly outperform orthogonal multiple access systems in terms of SE-EE tradeoff with the same level of rate fairness.

Balancing QoE and Fairness of Heterogeneous Traffics Based on Equivalent Rate Scaling

With great increase of mobile service in recent years,high quality of experience(QoE) is becoming a comprehensive and major goal for service provider.To unify evaluations of different services,mean opinion score(MOS) as a subjective assessment is usually adopted for accurate and convincing reflection of user perceived quality.In this paper,we consider the effect of the burst transmission of best effort(BE) traffic on the uses with real time video traffic in the same cell.We extend the rate scaling process which was initially used to shape burstiness of BE users as interference to handle the scenario that BE users act as resource competitors with video users.A power reallocation strategy between the two types of users is presented and an algorithm further improving the fairness of BE users is proposed.The simulation results demonstrate that the proposed algorithm can not only promote the QoE of both types of users,but also guarantee the fairness among users.

Fairness Aware Rate Adaptation and Proportional Scheduling for IEEE 802.11 WLANs Using FSE

With its rapid development in the wireless markets, IEEE 802.11 WLAN is experiencing a huge popularity. However, due to the limitation of frequency bandwidth of WLANs, it is essential that the available radio resource should be fully utilized to offer different services to multiple users. In order to maximize system throughput while still guaranteeing the fairness among users, a proportional fairness based algorithm is proposed in this work. Since most of the previous resource allocation algorithms were simply based on the channel conditions without taking into account user's demand, in this paper, we introduce the theory of fuzzy synthetic evaluation(FSE) which also allows us to consider user's demand as an important factor. As such, the fairness among users can be improved based on different users' requirements for services. In addition, a channel state information based rate adaptation scheme is also proposed. Through simulation studies, the results clearly validate that our proposed scheme shows advantages on providing user fairness while still improving the system throughput.

Dynamic Interference Coordination With Analytical Near-Optimal Power Allocation Toward High User Fairness

To mitigate interference on celledge users and improve fairness of the whole system, dynamic inter-cell interference coordination(ICIC) is one of the promising solutions. However, traditional dynamic ICIC is considered as an NP-hard problem and power variability further adds another dimension to this joint optimization issue, making it even more difficult to quickly reach a near-optimal solution. Therefore, we theoretically obtain the closed-form expression of the near-optimal power allocation ratio for users in adjacent cells paired in the same resource block and interfere each other, so that the total utility corresponding to α-fairness is maximized. Dynamic ICIC using this closed-form solution could improve user fairness without causing an increment of the computational complexity. Numerical results show that, compared with the schemes using identical power for different users, our method does not obviously degrade the system's average spectral efficiency.